title: On the combination of wavelet and curvelet for feature extraction to classify lung cancer on chest radiographs.
abstract: This paper investigates the combination of multiresolution methods for feature extraction for lung cancer. The focus is on the impact of combining wavelet and curvelet on the accuracy of the disease diagnosis. The paper investigates feature extraction with two different levels of wavelet, two different wavelet functions and the combination of wavelet and curvelet to obtain a high classification rate. The findings suggest the potential of combining different multiresolution methods in achieving high accuracy rates.
authors.forename: Hamada R H
authors.initials: HR
authors.lastname: Al-Absi
authors.forename: Brahim Belhaouari
authors.initials: BB
authors.lastname: Samir
authors.forename: Taha
authors.initials: T
authors.lastname: Alhersh
authors.forename: Suziah
authors.initials: S
authors.lastname: Sulaiman
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D013902:Radiography, Thoracic; D012680:Sensitivity and Specificity; D058067:Wavelet Analysis